Merge pull request #16 from MetExplore/development

Prepare for 0.9.0-beta.3

examples/exact_fastcore_example.py

@@ -2,7 +2,7 @@
 import numpy as np
 
 # Load a model
-m = miom.mio.load_gem('@homo_sapiens_human1_fcm.miom')
+m = miom.load_gem('@homo_sapiens_human1_fcm.miom')
 print("Reactions in the consistent iHuman GEM:", m.num_reactions)
 core_rxn = m.find_reactions_from_pathway("Cholesterol metabolism")
 print("Num. of core reactions:", sum(core_rxn))

examples/fba_example.py

@@ -1,7 +1,7 @@
 import miom
 
 # Load a model
-m = miom.mio.load_gem('https://github.com/pablormier/miom-gems/raw/main/gems/mus_musculus_iMM1865.miom')
+m = miom.load_gem('@mus_musculus_iMM1865.miom')
 
 target = 'BIOMASS_reaction'
 opt_flux = (miom.load(network=m, solver=miom.Solvers.GLPK)

examples/imat_example.py

@@ -1,6 +1,4 @@
-from miom import miom, Solvers
-from miom.mio import load_gem
-from miom.miom import Comparator, ExtractionMode
+import miom
 
 # Example implementation of iMAT using MIOM.
 # Note that this implementation supports also custom weights for the reactions
@@ -9,7 +7,7 @@
 # function is exactly the same as the original iMAT.
 
 # Use the iHuman-GEM model
-m = load_gem('https://github.com/pablormier/miom-gems/raw/main/gems/homo_sapiens_human1.miom')
+m = miom.load_gem('@homo_sapiens_human1.miom')
 
 # Add all the reactions from the Cholesterol pathway to the highly expressed set
 RH = m.find_reactions_from_pathway("Cholesterol metabolism")
@@ -18,14 +16,14 @@
 w = RH + RL
 print("RH:", sum(RH), "RL:", sum(abs(RL)))
 
-m = (miom(m, solver=Solvers.COIN_OR_CBC)
+m = (miom(m, solver=miom.Solvers.COIN_OR_CBC)
      .setup(int_tol=1e-8, opt_tol=0.01, verbosity=1)
      .steady_state()
      .subset_selection(w)
      .solve(max_seconds=30)
      .select_subnetwork(
-          mode=ExtractionMode.ABSOLUTE_FLUX_VALUE,
-          comparator=Comparator.GREATER_OR_EQUAL,
+          mode=miom.ExtractionMode.ABSOLUTE_FLUX_VALUE,
+          comparator=miom.Comparator.GREATER_OR_EQUAL,
           value=1e-8
      )
      .network)

examples/introduction_example.py

@@ -1,13 +1,13 @@
-from miom import miom, Solvers
-from miom.mio import load_gem
+import miom
 
 # Load a genome-scale metabolic network. You can load SMBL or Matlab metabolic networks
 # as well using the same method, but it requires to have the cobratoolbox python library
 # installed.
-network = load_gem("https://github.com/pablormier/miom-gems/raw/main/gems/mus_musculus_iMM1865.miom")
+network = miom.load_gem("@mus_musculus_iMM1865.miom")
 target_rxn = "BIOMASS_reaction"
 # Create the optimization problem with miom and solve
-model = (miom(network)
+model = (miom
+        .load(network)
         .steady_state()
         .set_rxn_objective(target_rxn)
         .solve(verbosity=1))

examples/sparse_fba_example.py

@@ -1,7 +1,7 @@
 import miom
 
 # Load a model
-m = miom.mio.load_gem('@mus_musculus_iMM1865.miom')
+m = miom.load_gem('@mus_musculus_iMM1865.miom')
 print("Num. of reactions in the network:", m.num_reactions)
 
 # Solve with Gurobi, CPLEX or CBC (other MIP solvers struggle with mediudm/large networks)

miom/__init__.py

@@ -5,5 +5,7 @@
     ExtractionMode
 )
 
-__all__ = ["load", "Solvers", "Comparator", "ExtractionMode"]
-__version__ = "0.9.0-beta.2"
+from miom.mio import load_gem
+
+__all__ = ["load", "load_gem", "Solvers", "Comparator", "ExtractionMode"]
+__version__ = "0.9.0-beta.3"

miom/__main__.py

@@ -17,6 +17,7 @@ def convert_list_gems(input_files, output=None):
             m = miom.mio.load_gem(input_file)
             print(f"Loaded network with {m.num_reactions} reactions (in-memory size: {m.object_size:.2f} MB)")
             # Concatenate folder and output file
+            print(os.path.abspath(output))
             if output is not None:
                 if isinstance(output, list) and len(output) == len(input_files):
                     output_file = output[i]
@@ -29,7 +30,7 @@ def convert_list_gems(input_files, output=None):
                             os.makedirs(abspath)
                         except OSError as e:
                             print(f"Cannot create {abspath} folder: {e}")
-                elif os.path.isdir(output):
+                elif os.path.isdir(output) or os.path.isdir(os.path.abspath(output)):
                     output_file = os.path.join(output, output_file)
                 else:
                     raise ValueError("The provided output is not a folder or a list of destinations for each file")

miom/mio.py

@@ -149,9 +149,9 @@ def load_gem(model_or_path):
         if ext == '.miom' or ext == '.xz' or ext == '.npz':
             return _load_compressed_model(file)
         else:
-            return _cobra_to_miom(_read_cobra_model(file))
+            return cobra_to_miom(_read_cobra_model(file))
     else:
-        return _cobra_to_miom(model_or_path)
+        return cobra_to_miom(model_or_path)
 
 
 def _read_cobra_model(filepath):
@@ -191,7 +191,7 @@ def export_gem(miom_network, path_to_exported_file):
             f_out.write(compressed)
 
 
-def _cobra_to_miom(model):
+def cobra_to_miom(model):
     try:
         from cobra.util.array import create_stoichiometric_matrix
     except ImportError as e:
@@ -219,11 +219,12 @@ def _cobra_to_miom(model):
             subsystems.append(rxn.subsystem.tolist())
         else:
             subsystems.append(rxn.subsystem)
-    rxn_data = [(rxn.id, rxn.lower_bound, rxn.upper_bound, subsystem, rxn.gene_reaction_rule)
+    rxn_data = [(rxn.id, rxn.name, rxn.lower_bound, rxn.upper_bound, subsystem, rxn.gene_reaction_rule)
                 for rxn, subsystem in zip(model.reactions, subsystems)]
     met_data = [(met.id, met.name, met.formula) for met in model.metabolites]
     R = np.array(rxn_data, dtype=[
         ('id', 'object'),
+        ('name', 'object'),
         ('lb', 'float'),
         ('ub', 'float'),
         ('subsystem', 'object'),

miom/miom.py

@@ -304,10 +304,10 @@ def indicator_values(self):
         return None
 
 
-def _partial(fn):
+def _composable(fn):
     """Annotation for methods that return the instance itself to enable chaining.
 
-    If a method `my_method` is annotated with @_partial, a method called `_my_method`
+    If a method `my_method` is annotated with @_composable, a method called `_my_method`
     is expected to be provided by a subclass. Parent method `my_method` is called first
     and the result is passed to the child method `_my_method`.
 
@@ -326,7 +326,7 @@ def wrapper(self, *args, **kwargs):
         # Find subclass implementation
         fname = '_' + fn.__name__
         if not hasattr(self, fname):
-            raise ValueError(f'Method "{fn.__name__}()" is marked as @_partial '
+            raise ValueError(f'Method "{fn.__name__}()" is marked as @_composable '
                              f'but the expected implementation "{fname}()" was not provided '
                              f'by {self.__class__.__name__}')
         func = getattr(self, fname)
@@ -403,7 +403,7 @@ def initialize_problem(self):
     def get_solver_status(self):
         pass
 
-    @_partial
+    @_composable
     def setup(self, **kwargs):
         """Provide the options for the solver.
 
@@ -444,7 +444,7 @@ def setup(self, **kwargs):
         self._options["_min_eps"] = np.sqrt(2*self._options["int_tol"])
         return self._options
 
-    @_partial
+    @_composable
     def steady_state(self):
         """Add the required constraints for finding steady-state fluxes
 
@@ -456,7 +456,7 @@ def steady_state(self):
         """
         pass
 
-    @_partial
+    @_composable
     def keep(self, reactions):
         """Force the inclusion of a list of reactions in the solution.
 
@@ -501,7 +501,7 @@ def keep(self, reactions):
                 if r.index in valid_rxn_idx]
         return dict(idxs=idxs, valid_rxn_idx=valid_rxn_idx)
 
-    @_partial
+    @_composable
     def subset_selection(self, rxn_weights, direction='max', eps=1e-2):
         """Transform the current model into a subset selection problem.
 
@@ -561,7 +561,7 @@ def subset_selection(self, rxn_weights, direction='max', eps=1e-2):
             warnings.warn("Indicator variables were already assigned")
             return False
 
-    @_partial
+    @_composable
     def set_flux_bounds(self, rxn_id, min_flux=None, max_flux=None):
         """Change the flux bounds of a reaction.
 
@@ -576,7 +576,7 @@ def set_flux_bounds(self, rxn_id, min_flux=None, max_flux=None):
         i, _ = self.network.find_reaction(rxn_id)
         return i
 
-    @_partial
+    @_composable
     def add_constraints(self, constraints):
         """Add a list of constraint to the model
 
@@ -591,7 +591,7 @@ def add_constraints(self, constraints):
             self.add_constraint(c)
         return len(constraints) > 0
 
-    @_partial
+    @_composable
     def add_constraint(self, constraint):
         """Add a specific constraint to the model.
         The constraint should use existing variables already included in the model.
@@ -601,7 +601,7 @@ def add_constraint(self, constraint):
         """
         pass
 
-    @_partial
+    @_composable
     def set_objective(self, cost_vector, variables, direction='max'):
         """Set the optmization objective of the model.
 
@@ -660,7 +660,7 @@ def set_fluxes_for(self, reactions, tolerance=1e-6):
         self.add_constraint(self.variables.fluxvars[i] <= ub)
         return self
 
-    @_partial
+    @_composable
     def reset(self):
         """Resets the original problem (removes all modifications)
 
@@ -730,7 +730,7 @@ def obtain_subnetwork(
         S_sub = S_sub[act_met, :]
         return MiomNetwork(S_sub, R_sub, M_sub)
 
-    @_partial
+    @_composable
     def select_subnetwork(
             self,
             mode=ExtractionMode.ABSOLUTE_FLUX_VALUE,
@@ -792,7 +792,7 @@ def get_fluxes(self, reactions=None):
         else:
             raise ValueError("reactions should be an iterable of strings or a single string")
 
-    @_partial
+    @_composable
     def solve(self, verbosity=None, max_seconds=None):
         """Solve the current model and assign the values to the variables of the model.
 
@@ -803,7 +803,7 @@ def solve(self, verbosity=None, max_seconds=None):
         """
         self._last_start_time = perf_counter()
 
-    @_partial
+    @_composable
     def copy(self):
         pass
 
@@ -912,7 +912,13 @@ def _select_subnetwork(self, **kwargs):
         return PicosModel(previous_step_model=self, miom_network=m)
 
     def _copy(self, **kwargs):
-        return PicosModel(previous_step_model=self)
+        m = PicosModel(previous_step_model=self)
+        # Create a copy of the internal problem
+        m.problem = self.problem.copy()
+        # TODO: Assign the new variables
+        m.variables._indicator_vars = None
+        m.variables._flux_vars = None
+        raise NotImplementedError("Will be added in future versions")
 
     def get_solver_status(self):
         return {
@@ -1051,7 +1057,10 @@ def _select_subnetwork(self, **kwargs):
         return PythonMipModel(previous_step_model=self, miom_network=kwargs["_parent_result"])
 
     def _copy(self, **kwargs):
-        return PythonMipModel(previous_step_model=self)
+        m = PythonMipModel(previous_step_model=self)
+        m.problem = self.problem.copy()
+        raise NotImplementedError("Will be added in future versions")
+
 
     def get_solver_status(self):
         #solver_status['elapsed_seconds'] = self.problem.search_progress_log.log[-1:][0][0]

mkdocs.yml

@@ -32,6 +32,8 @@ markdown_extensions:
   - pymdownx.highlight
   - pymdownx.superfences
   - pymdownx.details
+  - pymdownx.tabbed
+  - pymdownx.inlinehilite
   - pymdownx.arithmatex:
       generic: true
 extra:

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "miom"
-version = "0.9.0-beta.2"
+version = "0.9.0-beta.3"
 description = "Mixed Integer Optimization for Metabolism"
 authors = ["Pablo R. Mier <[email protected]>"]
 keywords = ["optimization", "LP", "MIP", "metabolism", "metabolic-networks"]

tests/test_miom.py

@@ -173,8 +173,5 @@ def test_keep_rxn(model):
     )
     assert abs(V[i]) > 1e-8
 
-def test_copy(model):
-    c = model.copy()
-    assert model != c and isinstance(c, BaseModel)